Automatic Recognition of Anatomical Regions in Computed Tomography Images

  • Márton József Tóth
    Affiliation

    Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1117 Budapest, Magyar tudósok körútja 2, Hungary

  • László Ruskó
    Affiliation

    GE Hungary Healthcare Division, H-6722 Szeged, Petőfi Sándor sgt 10, Hungary

  • Balázs Csébfalvi
    Affiliation

    Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1117 Budapest, Magyar tudósok körútja 2, Hungary

Abstract

This paper presents a method that can recognize anatomy regions in Computed Tomography (CT) examinations. In this work the human body is divided into eleven regions from the foot to the head. The proposed method consists of two main parts. In the first step, a Convolutional Neural Network (CNN) is used to classify the axial slices of the CT exam. The accuracy of the initial classification is 93.4 %. As the neural network processes the axial slices independently from each other, no spatial coherence is guaranteed. To ensure the contentious labeling the initial classification step is followed by a post-processing method that incorporates the expected order and size of the anatomical regions to improve the labeling. In this way, the accuracy is increased to 94.0 %, the confusion of non-neighboring regions dropped from 1.5 % to 0.0 %. This means that a continuous and outlier free labeling is obtained. The method was trained on a set of 320 CT exams and evaluated on another set of 160 cases.

Keywords: anatomy region recognition, deep learning, image classification, imaging informatics, medical image processing
Published online
2018-12-04
How to Cite
Tóth, M. J., Ruskó, L., Csébfalvi, B. “Automatic Recognition of Anatomical Regions in Computed Tomography Images”, Periodica Polytechnica Electrical Engineering and Computer Science, 62(4), pp. 117-125, 2018. https://doi.org/10.3311/PPee.12899
Section
Articles